Identification, classification and sorting of materials or objects has become increasingly important in many areas of industry. For example, plastic recycling is highly sensitive to front-end classification and sorting. Plastic recycling processes generally have low tolerances for material variation. Material misidentification accounts for a large proportion of variation in the recycling process as identification dictates which materials, and in what quantity, are fed into various recycling processes. By misidentifying one or more materials during the recycling process, the end recycled product may not meet a desired quality level.
Many sorting and identification methods rely on processing materials based upon passive methods relying on native properties of the materials themselves, such as molecular composition, melt temperature, and the like. However, the native properties are inherently limited in the amount of information that they can provide. Additionally, a reliance on the native properties does not always result in accurate classification and sorting. For example, a collection of heterogeneous plastics may all have a similar melting temperature while having distinct molecular compositions. Thus, relying on melting temperature would not result in a highly accurate sorting of the heterogeneous plastics.
Various new identification methods such as embedding forensic-like particles into the plastics, chemically bar-coding the plastics, or otherwise tagging the plastics at the time of manufactures are being incorporated for more reliable sorting during recycling. However, cost and implementation are challenging because of scale, increases in cost, difficult process integration, and other similar factors. Additionally, typical markings such as chemical bar-coding or embedding foreign particles alter the original properties of the plastic such as strength and transparency.